Text mining tools take on unstructured data

Summarizing the dataset can include measuring total instances of it occurring, a secondary metric occurring like social media engagements, or plotting results over time. Categorization is another natural language processing technique that can accomplish similar goals.

Sticking with the email dataset for example, what amount of emails included an email signature when they wrote to you? From this, can you learn what is the number one title or occupation your customers have? How about the third or fourth most likely occupation? Categorization can help enterprises understand who their customers are. Clustering is the most visually striking machine learning technique in my humble opinion , and again the goal here is to generate quick insight.

By clustering similarly themed data points in a visualization 1, below , the analyst is able to quickly see and exploit connections between different themes and ideas that may not have any similarity at first glance.

Clustering conversations around green mobility in the United States allows us to draw some interesting connections. Building a data lake data lake for your enterprise: A data lake is a central compository where all the data that could be useful and should be mined for insights by your data scientists and researchers lives. Within it you can apply text mining algorithms and other forms of deep learning algorithms i.

Sentiment analysis for text mining is one of the most powerful techniques to apply to natural language processing. With sentiment analysis applied to structured text data, enterprises can finally answer -- at scale and on a detailed level -- the big question they always want to know: Do people like this? Or absolutely not.

That nuance and judgement is difficult to incorporate into natural language processing. We compiled a list of some of the best tools to use for sentiment analysis. We also made a full list of text analytics tools that you should peruse. It also contains a useful glossary explaining in greater detail some of these text mining concepts like natural language processing and text mining.

Talkwalker is an open platform, which means that we support just about every variety of dataset and file source imaginable. This allows the data analyst to cross-compare against internal and external, owned, and third-party data sources. Clustering and visualizing the important topics of conversation in society is easy, and interesting, with the right toolset. Vectors represent different features of the existing data. The text data transformed into vectors, along with the expected predictions tags , is fed into a machine learning algorithm, creating a classification model:.

Then, the trained model can extract the relevant features of a new unseen text and make its own predictions over unseen information:. Naive Bayes family of algorithms NB : they benefit from Bayes Theorem and probability theory to predict the tag of a text.

In this case, vectors encode information based on the likelihood of words in a text belonging to any of the tags in the model. This probabilistic method can provide accurate results when there is not too much training data. Support Vector Machines SVM : this algorithm classifies vectors of tagged data into two different groups.

One that contains most of the vectors that belong to a given tag, and another one with the vectors that do not belong to that tag. The results of this algorithm are usually better than the results you get with Naive Bayes. However, it requires more coding power to train the model. Deep learning algorithms resemble the way the human brain thinks. By using millions of training examples, they generate very detailed representations of data and can create extremely accurate machine learning-based systems.

Hybrid systems combine rule-based systems with machine learning-based systems. They compliment each other to increase the accuracy of the results. The performance of a text classifier is measured through different parameters: accuracy , precision , recall and F1 score. Understanding these metrics will allow you to see how good your classifier model is at analyzing texts. This is a process that divides your training data into two subsets: a part of the data is used for training and the other part, for testing purposes.

This section will go through the different metrics to analyze the performance of your text classifier, and explain how cross-validation works:. Accuracy indicates the number of correct predictions that the classifier has made divided by the total number of predictions.

However, accuracy alone is not always the best metric to evaluate the performance of a classifier. Sometimes, when categories are imbalanced that means when there are many more examples for one category than for others , you may experience an accuracy paradox : the model is more likely to make a good prediction, as most of the data belongs to only one of the categories. Precision evaluates the number of correct predictions made by the classifier, over the total number of predictions for a given tag including both correct or incorrect predictions.

A high precision metric indicates there were less false positives. Recall indicates the number of texts that were predicted correctly, over the total number that should have been categorized with a given tag. A high recall metric means that there were less false negatives. This metric is particularly useful when you need to route support tickets to the right teams.

You want to automatically route as many tickets as possible for a particular tag for example Billing Issues at the expense of getting an incorrect prediction along the way. F1 score combines the parameters of precision and recall to give you an idea of how well your classifier is working.

This metric is a better indicator than accuracy to understand how good predictions are for all of the categories in your model. Cross-validation is frequently used to measure the performance of a text classifier. It consists of dividing the training data into different subsets, in a random way. Then, all of the subsets except one are used to train a text classifier. This text classifier is used to make predictions over the remaining subset of data testing.

The last step is compiling the results of all subsets of data to obtain an average performance of each metric. As we mentioned earlier, text extraction is the process of obtaining specific information from unstructured data. This has a myriad of applications in business. For instance, you could use it to extract company names out of a Linkedin dataset, or to identify different features on product descriptions.

All this, without actually having to read the data. Text extraction can be done using different methods. Regular expressions define a sequence of characters that can be associated with a tag. However, this method can be hard to scale, especially when patterns become more complex and require many regular expressions to determine an action.

Conditional Random Fields CRF is a statistical approach that can be used for text extraction with machine learning. It creates systems that learn the patterns they need to extract, by weighing different features from a sequence of words in a text. CRFs are capable of encoding much more information than Regular Expressions, enabling you to create more complex and richer patterns.

On the downside, more in-depth NLP knowledge and more computing power is required in order to train the text extractor properly. It is possible to evaluate text extractors by using the same performance metrics as text classification: accuracy , precision , recall and F1 score.

However, these metrics only consider exact matches as true positives, leaving partial matches aside. Suppose you create an address extractor. In this case, even though it is a partial match, it should not be considered as a false positive for the tag Address. ROUGE is a family of metrics that can be used to better evaluate the performance of text extractors than traditional metrics such as accuracy or F1.

How do they work? They calculate the lengths and number of sequences overlapping between the original text and the extraction extracted text.

The ROUGE metrics the parameters you would use to compare overlapping between the two texts mentioned above need to be defined manually.

Text mining makes it simple to analyze raw data on a large scale. What is text mining? Benefits of text mining include: Competitive intelligence Patent analysis Enhanced market research Better analysis of business data Transforming unstructured data Imagine this: You wake up, pick up your phone, and start scrolling through some of your applications.

All of this is unstructured, raw data. Challenges faced with text mining: While they can be easy to overcome, text mining has its own set of challenges. Pre-processing stage Some people rush over to the pre-processing stage of text mining, which means that the rules that have been defined are not entirely accurate. Tags: Document Processing. Follow me on LinkedIn. Where you can use Acodis? Legal Privacy Policy Imprint. Explore MonkeyLearn Studio , and see for yourself how easy it is to analyze and visualize your data all in one place your texts.

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

Best Text Mining Tools of To top it all off, you can take advantage of these text mining tools almost instantly. MonkeyLearn Best for: Small, medium and large businesses that want to extract valuable information and turn it into actionable insights. Aylien Best for: Developers who want to collect, analyze, and understand human-generated content at scale. Thematic Best for: Medium to large-sized companies who receive large volumes of customer feedback.

Google Cloud NLP Best for: Medium to large-sized companies that are looking for a pay-for-what-you-use service, for model building and predictive analytics. Amazon Comprehend Best for: Companies that require a low-learning curve product that enables high-level analysis of customer text data. MeaningCloud Best for: For developers of SMBs and large companies that want to extract meaning from unstructured content at an affordable price.

Lexalytics Best for: Medium to large-sized companies who process high volumes of data and require on-premise security or their own private cloud.

Final Words on Text Mining Tools Customer feedback and online interactions are a constant source of information for businesses. Posts you might like Text Analysis with Machine Learning Turn tweets, emails, documents, webpages and more into actionable data. Try MonkeyLearn.